Tools for Estimating VMT Reductions from Built Environment Changes

Introduction

The transport sector accounts for nearly half the greenhouse gas (GHG) emissions in Washington State (WSDOT 2009). Addressing transport sector GHG emissions is a priority for mitigating the impact of climate change and achieving environmental sustainability. Efforts include state-level legislation, regional climate action plans, and analysis of GHG impacts in environmental review (ICF International 2013; WSDOT 2009). One strategy for decreasing GHG emissions is reducing the amount of vehicle miles traveled (VMT) per capita. In Washington state, House Bill 2815 was passed in 2008 and established a target to reduce light-duty VMT per capita 18 percent by 2020, 30 percent by 2035, and 50 percent by 2050. Environmental sustainability, however, is just one aspect of a sustainable transportation system. Social equity and economic vitality comprise the other two principles of the “triple bottom line” definition of sustainability and must also be addressed to achieve a sustainable transportation (FHWA 2012). Therefore any sustainable VMT reduction strategy must simultaneously maintain or increase accessibility – the ability for people to reach desired goods, services, and activities (Litman 2012) – so that all three principles of the triple bottom line are met.

Accessibility can be achieved with fewer VMT when transit as well as walk, bike, and other non-motorized travel (NMT) modes are viable alternatives to the automobile. Washington State Department of Transportation (WSDOT) and its partners can support accessibility while reducing VMT and associated GHG emissions through plans, programs, and investments that support transit and NMT. To do so decision-makers must understand how proposed transportation and land use changes will affect travel. Traditionally, Metropolitan Planning Organizations (MPOs) and Regional Transportation Organizations (RTPO’s) use travel demand forecasting models to understand the impacts of alternative transportation investment scenarios. Unfortunately these models often fail to accurately forecast NMT (Transportation Research Board 2011). This is due to several factors: the relatively short distances these modes cover and the corresponding needs for high resolution data on land use and transportation systems conditions and infrastructure; the uncommon use of these travel modes beyond the more densely developed parts of towns, cities, and metropolitan areas; and the limited NMT travel data necessary to calibrate the models. Efforts are underway to address these shortcomings (Kuzmyak and Dill 2012)

Part of the research conducted for this project focused on identifying the indicators known to affect NMT and the tools that decision-makers can use to understand how proposed transportation and land use changes will affect travel. Extensive research has been conducted over the past two decades on the relationship between individual, household, land use and built environment (BE) factors associated with transit and NMT. This past research has provided a foundation for numerous tools that attempt to forecast the impact of land use and transportation system changes on transit, NMT, VMT, GHG emissions, and other travel-related outcomes. This report first summarizes the individual, household, and BE factors associated with NMT. It then reviews the tools that use those factors as inputs to predict travel behaviors and related outcomes.